New platform and new excitement? Exploring young educated sport customers' perceptions of watching live sports on OTT services
Bibliographic record
Abstract
Purpose This study aims to investigate how sports customers adopted over-the-top (OTT) services to consume sport content. Inspired by the technology acceptance model (TAM), the study aims to understand users' behavior when consuming sporting events and users' perceptions toward OTT services. Design/methodology/approach The participants of the study are Chinese sports consumers that use streaming services to watch live sport content. An online survey was distributed through HUPU Sports, a Chinese online communication community where sports fans can share opinions. To make sure all responses qualified to take part in the study, skip logic questions were added at the beginning of the questionnaire to ask participants to answer whether they used streaming services for watching sports. A total of 352 responses were received and there were 327 useable questionnaires. Findings The results revealed that viewing convenience, free of commercials and viewing quality were the main reasons impacting them to adopt OTT services. In terms of users' perceptions, paid users rated higher in perceived enjoyment, perceived value, perceived usefulness (PU) and ease of use than nonpaid users. OTT users' fandom and PU could predict the time the users spent on using these services, while the users' fandom and perceived value are positively related to the money users spent on these services. In addition, this study also found that users' fandom, perceived value, content quality, and ease of use are positively associated with users' intention to continue to use the service. Originality/value The study is one of the first attempts to explore how sports audiences adopted OTT services to consume sport content and explore the audiences' perceptions toward OTT usage. Previous studies have already investigated how users adopted music streaming services (Fernandes and Guerra, 2019) and other online streaming services (Shin and Park, 2021), but little attention has been given to sports streaming services specifically. Therefore, the findings of the study fill the gap in the extant knowledge of sport consumers' behavior and provide more insights to their online behaviors. Moreover, the authors also contribute to the growing digital media literature by advancing our understanding perceptional differences between paid users and unpaid users. The streaming services literature has primarily focused on general users (Fernandes and Guerra, 2019), but the services neglect to understand the differences in between paid and unpaid users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".